International Research Journal of Education and Technology

This study examines current developments in machine learning (ML)-based crop price prediction models, with an emphasis on enhancing agricultural planning in areas where farming is the primary source of income for rural populations. Traditional approaches based on farmers' prior experiences are increasingly insufficient as agricultural economies deal with growing market and climate uncertainty. Researchers have created predictive models that consider a variety of data sources, such as past crop prices, soil types, weather trends, and socioeconomic characteristics, in order to address these issues. The review covers important research from 2019 to 2024, emphasizing the work of authors whose models, which included algorithms including Random Forest, Decision Tree, and Ensemble techniques, were able to predict crop prices with up to 97% accuracy. This excellent prediction accuracy facilitates well-informed choices for farmers, facilitating proactive crop selection and resource management, lowering financial risk, and increasing profitability. The paper also discusses the main drawbacks of the models that are currently in use, including the difficulty of generalizing forecasts across various geographic locations and restrictions in data availability. More flexible, hybrid models that can manage these constraints and expand to larger agricultural contexts should be investigated in future research. This paper highlights how ML-driven crop price prediction can promote data-driven, sustainable farming methods, which will ultimately help agro-based communities' food security and economic resilience.

Key Words:  Crop Price Prediction, Machine Learning, Risk Management, Agriculture, Random Forest, Decision Trees